KDD2017 video
KDD 2017 is the 23rd SIGKDD Conference on Knowledge Discovery and Data Mining, one of the world’s largest and best Data Science conferences. Started in 1989, KDD is the oldest & largest data mining conference worldwide. We pioneered “Big Data”, “Data Science”, and “Predictive Analytics” solutions before these names even existed – some of the first & most highly cited research papers on these topics were published in our conference. Other notable innovations that originated in our conference include crowd sourcing; large scale data mining competitions with over 10,000 participants, personalized advertising, e.g., on Google, graph mining algorithms that power Facebook & LinkedIn, and recommender systems used by Netflix, Amazon, etc. After 25 years and an explosive growth in this industry, we are still the home for the latest cutting-edge research in these topics. Even today, the technology & research discussed at our conference is often 1-3 years ahead of any other conference!
A/B Testing in Networks with Adversarial Members
Learning to Make Stuff
Cultural Creativity
Stacked Ensemble Models and Data Science Competitions
Managing Research Team Panel
Mentoring Session Panel
Profit Maximization for Online Advertising Demand-Side Platform
MM2RTB: Bring Multimedia Metrics to Real-Time Bidding
Ranking and Calibrating Click-Attributed Purchases in Performance Display Advertising
Machine Learning and Causal Inference for Advertising Effectiveness
Deep & Cross Network for Ad Click Predictions
Blacklisting the Blacklist in Online Advertising
Attribution Modeling Increases Efficiency of Bidding in Display Advertising
Data-Driven Reserve Prices for Social Advertising Auctions at LinkedIn
Incrementality Bidding & Attribution
Cost-sensitive Learning for Utility Optimization in Online Advertising Auctions
Optimal Reserve Price for Online Ads Trading Based on Inventory Identification
Learning from Logged Interventions
An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy
A Hybrid Approach for Sentiment Analysis Applied to Paper
Deep Learning for Contrasting Meaning Representation and Composition
Viscovery: A Platform for Trend Tracking in Opinion Forums
Creating Domain-Specific Sentiment Lexicons via Text Mining
Barycentric coordinates for ordinal sentiment classification
From Bioinformatics to Precision Medicine
Cost-sensitive Deep Learning for Early Readmission Prediction at A Major Hospital
Improving the Prediction of Functional Outcome in Ischemic Stroke Patients
Ontology-based workflow extraction from texts using word sense disambiguation
Coordination Event Detection and Initiator Identification in Time Series Data
Robust Parameter-Free Season Length Detection in Time Series